Sandor F. Toth
Individual tutorial study of topics or courses under development to address the latest scientific developments in forest resources. Offered: AWSpS.
This is a 5-credit problem-oriented course where the instructor's research projects serve as vehicles for introducing advanced analytical decision tools that can aid natural resource management. We will focus on spatial optimization problems in conservation, ecosystem management, landscape-level forest planning, conflict resolution and invasive species management. The following mathematical techniques will be covered: linear-, integer- and multi-objective programming, stochastic and combinatorial optimization and Markovian decision processes. The emphasis will be on model building rather than on algorithmic concepts. The students will learn how to select the most appropriate tools for various situations, how to use them, and how to interpret the results that these tools provide.
The target audience of the course includes both graduate and undergraduate students who are interested in applying quantitative skills to environmental problems. Those with strong math or engineering backgrounds will learn how to approach and conceptualize complex, seemingly un-quantifiable but real problems in the area of natural resource management. Those with strong biological, ecological or social science backgrounds will learn how handy mathematical modeling can be in providing answers to some familiar but challenging environmental questions.
Student learning goals
By the end of this course, students should be able to decide if it makes sense to use a spatial optimization technique to address a particular natural resource problem.
Understand the pros and cons of the various optimization modeling techniques.
Formulate and solve mathematical programming models using commercial software, and interpret the solutions that these model provide.
Understand the basics of stochastic optimization and Markovian decision processes as they are applied to natural resource problems.
General method of instruction
A combination of lectures, discussions and labs.
No specific quantitative background is required.
Class assignments and grading
Homework Assignments (8): Individual work Lab assignments (6): Work in groups of 4 Quizzes (5): Closed-notes Midterm Exam: Open-notes Final Exam (comprehensive): Open-notes
Note: The actual number of assignments might be lower but not higher. The random quizzes will take no longer than 5-10 minutes to complete.
Homework Assignments: 25% Lab assignments: 15% Random Quizzes: 10% Midterm Exam: 25% Final Exam (comprehensive): 25%